Regression Analysis

Regression Analysis

This course will teach you how multiple linear regression models are derived, the use software to implement them, what assumptions underlie the models, how to test whether your data meet those assumptions and what can be done when those assumptions are not met, and develop strategies for building and understanding useful models.

This course will teach you how multiple linear regression models are derived, assumptions in the models, how to test whether data meets assumptions, and develop strategies for building and understanding useful models.

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Overview

Regression is perhaps the most widely used statistical technique. It estimates relationships between independent variables and a dependent variables. Regression models can be used to help understand and explain relationships among variables; they can also be used to predict actual outcomes.

In this course you will learn how to derive multiple linear regression models, how to use software to implement them, and what assumptions underlie the models. You will also learn how to test whether your data meets those assumptions, what can be done when those assumptions are not met, and strategies to build and understand useful models.

Learning Outcomes

After completing this course you should be able to calculate both simple and multiple regression models. You will learn how to assess the model’s “fit”, test model assumptions, and transform predictor and response variables to improve outcomes. You will also learn to identify critical aspects of the data that can influence results of your model and how to exercise caution with respect to extrapolation from regression results.

  • Calculate a simple linear regression model
  • Assess the model with standard error, R-squared, and slope
  • Review and check model assumptions
  • Extend the model to multiple linear regression
  • Assess parameter estimates globally, in subsets, and individually
  • Test model assumptions
  • Deal with qualitative predictors
  • Transform predictors and response variables to improve model fit
  • Handle interactions among predictors
  • Identify influential points 
  • Deal with autocorrelation, multicollinearity, and missing data
  • Exercise appropriate caution with respect to extrapolation

Who Should Take This Course

Scientists, business analysts, engineers and researchers who need to model relationships in data in which a single response variable depends on multiple predictor variables. If you were introduced to regression in an introductory statistics course and now find you need a more solid grounding in the subject, this course is for you. If you are planning to learn additional topics in statistics, a good knowledge of regression is often essential.

Instructors

dr-iain-pardoe

Dr. Iain Pardoe

Dr. Iain Pardoe teaches online and writes courses for Thompson Rivers University Open Learning.  He also does statistical consulting and was formerly an Associate Professor of Decision Sciences at the University of Oregon Lundquist College of Business. His research specialty is in the area of multivariate modeling. He has numerous journal publications (including a noted paper in the Journal of the Royal Statistical Society on predicting Academy Award winners).
See Instructor Bio

Course Syllabus

Week 1

Foundations and Simple Linear Regression

  • Brief review of univariate statistical ideas:confidence intervals
    hypothesis testing
    prediction
  • confidence intervals
  • hypothesis testing
  • prediction
  • Simple linear regression model and least squares estimation
  • Model evaluation:regression standard error
    R-squared
    testing the slope
  • regression standard error
  • R-squared
  • testing the slope
  • Checking model assumptions
  • Estimation and prediction

Week 2

Multiple Linear Regression

  • Multiple linear regression model and least squares estimation
  • Model evaluation:regression standard error
    R-squared
    testing the regression parameters globally
    testing the regression parameters in subsets
    testing the regression parameters individually
  • regression standard error
  • R-squared
  • testing the regression parameters globally
  • testing the regression parameters in subsets
  • testing the regression parameters individually
  • Checking model assumptions
  • Estimation and prediction

Week 3

Model Building I

  • Predictor transformations
  • Response transformations
  • Predictor interactions
  • Qualitative predictors and the use of indicator variables

Week 4

Model Building II

  • Influential points (outliers and leverage)
  • Autocorrelation
  • Multicollinearity
  • Excluding important predictors
  • Overfitting
  • Extrapolation
  • Missing data
  • Model building guidelines
  • Model interpretation using graphics

Class Dates

2019

Oct 4, 2019 to Nov 1, 2019

2020

Jan 17, 2020 to Feb 14, 2020

May 8, 2020 to Jun 5, 2020

Oct 2, 2020 to Oct 30, 2020

2021

No classes scheduled at this time.

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Prerequisites

Introductory Statistics

We assume you are versed in statistics or have the equivalent understanding of topics covered in our Statistics 1 and Statistics 2 courses. but do not require them as eligibility to enroll in this course. Please review the course description for each of our introductory statistics courses, estimate which best matches your level of understanding of the material covered in these courses, then take the short assessment test for that course. If you can not answer more than half of the questions correctly, we suggest you take our Statistics 1 and Statistics 2 courses prior to taking this course.

Recommended

We recommended, but do not require as eligibility to enroll in this course, an understanding of the material covered in these following courses.

Statistics 1 – Probability and Study Design

This course, the first of a three-course sequence, provides an introduction to statistics for those with little or no prior exposure to basic probability and statistics.
Topic: Statistics, Introductory Statistics | Skill: Introductory | Credit Options: ACE, CAP, CEU

Statistics 2 – Inference and Association

This course, the second of a three-course sequence, will teach you the use of inference and association through a series of practical applications, based on the resampling/simulation approach, and how to test hypotheses, compute confidence intervals regarding proportions or means, computer correlations, and use of simple linear regressions.
Topic: Statistics, Introductory Statistics | Skill: Introductory | Credit Options: ACE, CEU
Class Start Dates: Dec 6, 2019

Statistics 3 – ANOVA and Regression

This course, the third of a three-course sequence, provides ananalysis of variance (ANOVA) and multiple linear regression through a series of practical applications.
Topic: Statistics, Introductory Statistics | Skill: Introductory, Intermediate | Credit Options: CEU

What Our Students Say​

I fully enjoyed the challenge of working through the assignments and reading Dr. Pardoe's book. I gained some new insights into regression analysis. In other words, my horizons were expanded and I learned a great deal.

Phillip Palmer
Philadelphia College of Osteopathic Medicine

One of the best classes I've taken with Statistics.com

Katie Healey
Ecofish Research Ltd

Frequently Asked Questions

Can I transfer or withdraw from a course?

We have a flexible transfer and withdrawal policy that recognizes circumstances may arise to prevent you from taking a course as planned. You may transfer or withdraw from a course under certain conditions.

  • Students are entitled to a full refund if a course they are registered for is canceled.
  • You can transfer your tuition to another course at any time prior to the course start date or the drop date, however a transfer is not permitted after the drop date.
  • Withdrawals on or after the first day of class are entitled to a percentage refund of tuition.

Please see this page for more information.

Who are the instructors at the Institute?

The Institute has more than 60 instructors who are recruited based on their expertise in various areas in statistics. Our faculty members are:

  • Authors of well-regarded texts in their area;
  • Advisory board members;
  • Senior faculty; and
  • Educators who have made important contributions to the field of statistics or online education in statistics.

The majority of our instructors have more than five years of teaching experience online at the Institute.

Please visit our faculty page for more information on each instructor at The Institute for Statistics Education.

Please see our knowledge center for more information.

What type of courses does the Institute offer?

The Institute offers approximately 80 courses each year. Topics include basic survey courses for novices, a full sequence of introductory statistics courses, bridge courses to more advanced topics. Our courses cover a range of topics including biostatistics, research statistics, data mining, business analytics, survey statistics, and environmental statistics.

Please see our course search or knowledge center for more information.

Do your courses have for-credit options?

Our courses have several for-credit options:

  • Continuing education units (CEU)
  • College credit through The American Council on Education (ACE CREDIT)
  • Course credits that are transferable to the INFORMS Certified Analytics Professional (CAP®)

Please see our knowledge center for more information.

Is the Institute for Statistics Education certified?

The Institute for Statistics Education is certified to operate by the State Council of Higher Education for Virginia (SCHEV). For more information visit: http://www.schev.edu

Please see our knowledge center for more information.

Visit our knowledge base and learn more.

FAQs + Knowledge Base

Related Courses

Generalized Linear Models

This course will explain the theory of generalized linear models (GLM), outline the algorithms used for GLM estimation, and explain how to determine which algorithm to use for a given data analysis.
Topic: Statistics, Statistical Modeling | Skill: Intermediate, Advanced | Credit Options: CEU
Class Start Dates: Apr 10, 2020

Logistic Regression

This course will teach you logistic regression ordinary least squares (OLS) methods to model data with binary outcomes rather than directly estimating the value of the outcome, logistic regression allows you to estimate the probability of a success or failure.
Topic: Statistics, Statistical Modeling | Skill: Intermediate | Credit Options: CAP, CEU
Class Start Dates: Jan 10, 2020, Jul 17, 2020

Additional Course Information

Organization of Course

This course takes place online at The Institute for 4 weeks. During each course week, you participate at times of your own choosing – there are no set times when you must be online. Course participants will be given access to a private discussion board. In class discussions led by the instructor, you can post questions, seek clarification, and interact with your fellow students and the instructor.

At the beginning of each week, you receive the relevant material, in addition to answers to exercises from the previous session. During the week, you are expected to go over the course materials, work through exercises, and submit answers. Discussion among participants is encouraged. The instructor will provide answers and comments, and at the end of the week, you will receive individual feedback on your homework answers.

Time Requirements

This is a 4-week course requiring 10-15 hours per week of review and study, at times of your choosing.

Homework

Homework in this course consists of short answer questions to test concepts and guided data analysis problems using software.

Course Text

The required text for this course is Applied Regression Modeling, Second Edition by Iain Pardoe.

Please order a copy of your course textbook prior to course start date.

Software

You will need software that is capable of doing regression analysis, which all statistical software does.  If you are undecided about which package to choose, consider the following:

1.  If you are likely to take additional statistical modeling courses and intend to apply these methods to your research, you should choose a standard package with power and flexibility (R, SAS, JMP, SPSS, Minitab, Stata).

2.  If your plans include applications of data science and data analytics in business, you should probably choose R (if your company already uses SAS or SPSS, that’s also fine).

3.  If you want to work as a manager or analyst in business, but not as a data scientist, you could use an Excel add-in like XLMiner.

4.  If you have no immediate plans for further coursework and a short learning curve is your main consideration, consider Statcrunch, JMP or Minitab.

The instructor is most familiar with R and Minitab. There will be some supplementary materials in the course to provide assistance with R, SPSS, Minitab, SAS, JMP, EViews, Stata, and Statistica. Our teaching assistants can offer some help with R, Minitab, SAS, JMP, Stata, Excel, and StatCrunch.

Software Uses and Descriptions | Available Free Versions
To learn more about the software used in this course, or how to obtain free versions of software used in our courses, please read our knowledge base article “What software is used in courses?” 

 

Course Fee & Information

Enrollment
Courses may fill up at any time and registrations are processed in the order in which they are received. Your registration will be confirmed for the first available course date unless you specify otherwise.

Transfers and Withdrawals
We have flexible policies to transfer to another course or withdraw if necessary.

Group Rates
Contact us to get information on group rates.

Discounts
Academic affiliation?  In most courses you are eligible for a discount at checkout.

New to Statistics.com?  Click here for a special introductory discount code.  

Invoice or Purchase Order
Add $50 service fee if you require a prior invoice, or if you need to submit a purchase order or voucher, pay by wire transfer or EFT, or refund and reprocess a prior payment.

Options for Credit and Recognition

This course is eligible for the following credit and recognition options:

No Credit
You may take this course without pursuing credit or a record of completion.

Mastery or Certificate Program Credit
If you are enrolled in mastery or certificate program that requires demonstration of proficiency in this subject, your course work may be assessed for a grade.

CEUs and Proof of Completion
If you require a “Record of Course Completion” along with professional development credit in the form of Continuing Education Units (CEU’s), upon successfully completing the course, CEU’s and a record of course completion will be issued by The Institute upon your request.

ACE CREDIT | College Credit
This course has been evaluated by the American Council on Education (ACE) and is recommended for the the upper division baccalaureate degree, 3 semester hours in data mining, statistics, or computer science. Please note that the decision to accept specific credit recommendations is up to the academic institution accepting the credit.

ACE Digital Badge
Courses evaluated by the American Council on Education (ACE) have a digital badge available for successful completion of the course.

INFORMS-CAP
This course is recognized by the Institute for Operations Research and the Management Sciences (INFORMS) as helpful preparation for the Certified Analytics Professional (CAP®) exam and can help CAP® analysts accrue Professional Development Units to maintain their certification.

Supplemental Information

There is no supplemental content for this course.

Miscellaneous

There is no additional information for this course.

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Regression Analysis
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